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In an effort to overcome the limitations of current reverse engineering algorithms and possible biases due to the microarray platform considered [16], in this paper we consider simultaneously five different algorithms and rely on datasets from two different platforms (cDNA and Affymetrix technologies).
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Of these genes, 1,701 are represented on the Affymetrix HG-U95A2 microarray platform, common to all data sets considered in the present study (Table S1) Although the array covers only about 40% of the whole Membranome (Fig. 1A), the internal representation of all major functional classes – as defined by Panther [22] - is strictly maintained (Fisher exact test p-value<0.001 p-value<0.001
However, it should be recognized that other factors, such as specificity of the arrays to detect the targeted gene, validity of results identifying differential gene expression, and multiple sources of variation, should be considered for any microarray platform.
'Present' on one microarray platform but not the other, which are considered a separate set of discrepant results.
Consequently, data produced so far using different microarray platforms, can only be considered by focusing on the highly differentially expressed genes.
For example, a gene that is listed in two studies that employ different microarray platforms and statistical methods could be considered more meaningful than a gene that is listed in two studies that employ the same microarray platform and statistical methodology.
We considered that the agreement of detected microRNAs in each sample and differentially expressed microRNAs between two samples for each microarray platform.
Differences between the predicted and reported (e.g. literature, databases) gene relationships may be due to differences among the experiments considered (e.g. honey bee versus fruit fly) and insufficient or inaccurate information (e.g. small experiment, limited microarray platform).
Then select the microarray platform and specific experiment to search against and the display format.
The accumulated transcribed sequences could be directly used to develop microarray platform.
The design, construction and application of a Pinus microarray platform are described.
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